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spaCy/spacy/tests/parser/test_parse.py
Ines Montani f90482d077 Tidy up and auto-format
2021-07-18 15:44:56 +10:00

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import pytest
from numpy.testing import assert_equal
from spacy.attrs import DEP
from spacy.lang.en import English
from spacy.training import Example
from spacy.tokens import Doc
from spacy import util, registry
from ..util import apply_transition_sequence, make_tempdir
from ...pipeline import DependencyParser
from ...pipeline.dep_parser import DEFAULT_PARSER_MODEL
TRAIN_DATA = [
(
"They trade mortgage-backed securities.",
{
"heads": [1, 1, 4, 4, 5, 1, 1],
"deps": ["nsubj", "ROOT", "compound", "punct", "nmod", "dobj", "punct"],
},
),
(
"I like London and Berlin.",
{
"heads": [1, 1, 1, 2, 2, 1],
"deps": ["nsubj", "ROOT", "dobj", "cc", "conj", "punct"],
},
),
]
CONFLICTING_DATA = [
(
"I like London and Berlin.",
{
"heads": [1, 1, 1, 2, 2, 1],
"deps": ["nsubj", "ROOT", "dobj", "cc", "conj", "punct"],
},
),
(
"I like London and Berlin.",
{
"heads": [0, 0, 0, 0, 0, 0],
"deps": ["ROOT", "nsubj", "nsubj", "cc", "conj", "punct"],
},
),
]
PARTIAL_DATA = [
(
"I like London.",
{
"heads": [1, 1, 1, None],
"deps": ["nsubj", "ROOT", "dobj", None],
},
),
]
eps = 0.1
def test_parser_root(en_vocab):
words = ["i", "do", "n't", "have", "other", "assistance"]
heads = [3, 3, 3, 3, 5, 3]
deps = ["nsubj", "aux", "neg", "ROOT", "amod", "dobj"]
doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
for t in doc:
assert t.dep != 0, t.text
@pytest.mark.skip(
reason="The step_through API was removed (but should be brought back)"
)
@pytest.mark.parametrize("words", [["Hello"]])
def test_parser_parse_one_word_sentence(en_vocab, en_parser, words):
doc = Doc(en_vocab, words=words, heads=[0], deps=["ROOT"])
assert len(doc) == 1
with en_parser.step_through(doc) as _: # noqa: F841
pass
assert doc[0].dep != 0
@pytest.mark.skip(
reason="The step_through API was removed (but should be brought back)"
)
def test_parser_initial(en_vocab, en_parser):
words = ["I", "ate", "the", "pizza", "with", "anchovies", "."]
transition = ["L-nsubj", "S", "L-det"]
doc = Doc(en_vocab, words=words)
apply_transition_sequence(en_parser, doc, transition)
assert doc[0].head.i == 1
assert doc[1].head.i == 1
assert doc[2].head.i == 3
assert doc[3].head.i == 3
def test_parser_parse_subtrees(en_vocab, en_parser):
words = ["The", "four", "wheels", "on", "the", "bus", "turned", "quickly"]
heads = [2, 2, 6, 2, 5, 3, 6, 6]
deps = ["dep"] * len(heads)
doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
assert len(list(doc[2].lefts)) == 2
assert len(list(doc[2].rights)) == 1
assert len(list(doc[2].children)) == 3
assert len(list(doc[5].lefts)) == 1
assert len(list(doc[5].rights)) == 0
assert len(list(doc[5].children)) == 1
assert len(list(doc[2].subtree)) == 6
def test_parser_merge_pp(en_vocab):
words = ["A", "phrase", "with", "another", "phrase", "occurs"]
heads = [1, 5, 1, 4, 2, 5]
deps = ["det", "nsubj", "prep", "det", "pobj", "ROOT"]
pos = ["DET", "NOUN", "ADP", "DET", "NOUN", "VERB"]
doc = Doc(en_vocab, words=words, deps=deps, heads=heads, pos=pos)
with doc.retokenize() as retokenizer:
for np in doc.noun_chunks:
retokenizer.merge(np, attrs={"lemma": np.lemma_})
assert doc[0].text == "A phrase"
assert doc[1].text == "with"
assert doc[2].text == "another phrase"
assert doc[3].text == "occurs"
@pytest.mark.skip(
reason="The step_through API was removed (but should be brought back)"
)
def test_parser_arc_eager_finalize_state(en_vocab, en_parser):
words = ["a", "b", "c", "d", "e"]
# right branching
transition = ["R-nsubj", "D", "R-nsubj", "R-nsubj", "D", "R-ROOT"]
tokens = Doc(en_vocab, words=words)
apply_transition_sequence(en_parser, tokens, transition)
assert tokens[0].n_lefts == 0
assert tokens[0].n_rights == 2
assert tokens[0].left_edge.i == 0
assert tokens[0].right_edge.i == 4
assert tokens[0].head.i == 0
assert tokens[1].n_lefts == 0
assert tokens[1].n_rights == 0
assert tokens[1].left_edge.i == 1
assert tokens[1].right_edge.i == 1
assert tokens[1].head.i == 0
assert tokens[2].n_lefts == 0
assert tokens[2].n_rights == 2
assert tokens[2].left_edge.i == 2
assert tokens[2].right_edge.i == 4
assert tokens[2].head.i == 0
assert tokens[3].n_lefts == 0
assert tokens[3].n_rights == 0
assert tokens[3].left_edge.i == 3
assert tokens[3].right_edge.i == 3
assert tokens[3].head.i == 2
assert tokens[4].n_lefts == 0
assert tokens[4].n_rights == 0
assert tokens[4].left_edge.i == 4
assert tokens[4].right_edge.i == 4
assert tokens[4].head.i == 2
# left branching
transition = ["S", "S", "S", "L-nsubj", "L-nsubj", "L-nsubj", "L-nsubj"]
tokens = Doc(en_vocab, words=words)
apply_transition_sequence(en_parser, tokens, transition)
assert tokens[0].n_lefts == 0
assert tokens[0].n_rights == 0
assert tokens[0].left_edge.i == 0
assert tokens[0].right_edge.i == 0
assert tokens[0].head.i == 4
assert tokens[1].n_lefts == 0
assert tokens[1].n_rights == 0
assert tokens[1].left_edge.i == 1
assert tokens[1].right_edge.i == 1
assert tokens[1].head.i == 4
assert tokens[2].n_lefts == 0
assert tokens[2].n_rights == 0
assert tokens[2].left_edge.i == 2
assert tokens[2].right_edge.i == 2
assert tokens[2].head.i == 4
assert tokens[3].n_lefts == 0
assert tokens[3].n_rights == 0
assert tokens[3].left_edge.i == 3
assert tokens[3].right_edge.i == 3
assert tokens[3].head.i == 4
assert tokens[4].n_lefts == 4
assert tokens[4].n_rights == 0
assert tokens[4].left_edge.i == 0
assert tokens[4].right_edge.i == 4
assert tokens[4].head.i == 4
def test_parser_set_sent_starts(en_vocab):
# fmt: off
words = ['Ein', 'Satz', '.', 'Außerdem', 'ist', 'Zimmer', 'davon', 'überzeugt', ',', 'dass', 'auch', 'epige-', '\n', 'netische', 'Mechanismen', 'eine', 'Rolle', 'spielen', ',', 'also', 'Vorgänge', ',', 'die', '\n', 'sich', 'darauf', 'auswirken', ',', 'welche', 'Gene', 'abgelesen', 'werden', 'und', '\n', 'welche', 'nicht', '.', '\n']
heads = [1, 1, 1, 30, 4, 4, 7, 4, 7, 17, 14, 14, 11, 14, 17, 16, 17, 6, 17, 20, 11, 20, 26, 22, 26, 26, 20, 26, 29, 31, 31, 25, 31, 32, 17, 4, 4, 36]
deps = ['nk', 'ROOT', 'punct', 'mo', 'ROOT', 'sb', 'op', 'pd', 'punct', 'cp', 'mo', 'nk', '', 'nk', 'sb', 'nk', 'oa', 're', 'punct', 'mo', 'app', 'punct', 'sb', '', 'oa', 'op', 'rc', 'punct', 'nk', 'sb', 'oc', 're', 'cd', '', 'oa', 'ng', 'punct', '']
# fmt: on
doc = Doc(en_vocab, words=words, deps=deps, heads=heads)
for i in range(len(words)):
if i == 0 or i == 3:
assert doc[i].is_sent_start is True
else:
assert doc[i].is_sent_start is False
for sent in doc.sents:
for token in sent:
assert token.head in sent
def test_parser_constructor(en_vocab):
config = {
"learn_tokens": False,
"min_action_freq": 30,
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
DependencyParser(en_vocab, model, **config)
DependencyParser(en_vocab, model)
@pytest.mark.parametrize("pipe_name", ["parser", "beam_parser"])
def test_incomplete_data(pipe_name):
# Test that the parser works with incomplete information
nlp = English()
parser = nlp.add_pipe(pipe_name)
train_examples = []
for text, annotations in PARTIAL_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for dep in annotations.get("deps", []):
if dep is not None:
parser.add_label(dep)
optimizer = nlp.initialize(get_examples=lambda: train_examples)
for i in range(150):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
assert losses[pipe_name] < 0.0001
# test the trained model
test_text = "I like securities."
doc = nlp(test_text)
assert doc[0].dep_ == "nsubj"
assert doc[2].dep_ == "dobj"
assert doc[0].head.i == 1
assert doc[2].head.i == 1
@pytest.mark.parametrize("pipe_name", ["parser", "beam_parser"])
def test_overfitting_IO(pipe_name):
# Simple test to try and quickly overfit the dependency parser (normal or beam)
nlp = English()
parser = nlp.add_pipe(pipe_name)
train_examples = []
for text, annotations in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for dep in annotations.get("deps", []):
parser.add_label(dep)
optimizer = nlp.initialize()
# run overfitting
for i in range(200):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
assert losses[pipe_name] < 0.0001
# test the trained model
test_text = "I like securities."
doc = nlp(test_text)
assert doc[0].dep_ == "nsubj"
assert doc[2].dep_ == "dobj"
assert doc[3].dep_ == "punct"
assert doc[0].head.i == 1
assert doc[2].head.i == 1
assert doc[3].head.i == 1
# Also test the results are still the same after IO
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
nlp2 = util.load_model_from_path(tmp_dir)
doc2 = nlp2(test_text)
assert doc2[0].dep_ == "nsubj"
assert doc2[2].dep_ == "dobj"
assert doc2[3].dep_ == "punct"
assert doc2[0].head.i == 1
assert doc2[2].head.i == 1
assert doc2[3].head.i == 1
# Make sure that running pipe twice, or comparing to call, always amounts to the same predictions
texts = [
"Just a sentence.",
"Then one more sentence about London.",
"Here is another one.",
"I like London.",
]
batch_deps_1 = [doc.to_array([DEP]) for doc in nlp.pipe(texts)]
batch_deps_2 = [doc.to_array([DEP]) for doc in nlp.pipe(texts)]
no_batch_deps = [doc.to_array([DEP]) for doc in [nlp(text) for text in texts]]
assert_equal(batch_deps_1, batch_deps_2)
assert_equal(batch_deps_1, no_batch_deps)
def test_beam_parser_scores():
# Test that we can get confidence values out of the beam_parser pipe
beam_width = 16
beam_density = 0.0001
nlp = English()
config = {
"beam_width": beam_width,
"beam_density": beam_density,
}
parser = nlp.add_pipe("beam_parser", config=config)
train_examples = []
for text, annotations in CONFLICTING_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for dep in annotations.get("deps", []):
parser.add_label(dep)
optimizer = nlp.initialize()
# update a bit with conflicting data
for i in range(10):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
# test the scores from the beam
test_text = "I like securities."
doc = nlp.make_doc(test_text)
docs = [doc]
beams = parser.predict(docs)
head_scores, label_scores = parser.scored_parses(beams)
for j in range(len(doc)):
for label in parser.labels:
label_score = label_scores[0][(j, label)]
assert 0 - eps <= label_score <= 1 + eps
for i in range(len(doc)):
head_score = head_scores[0][(j, i)]
assert 0 - eps <= head_score <= 1 + eps
def test_beam_overfitting_IO():
# Simple test to try and quickly overfit the Beam dependency parser
nlp = English()
beam_width = 16
beam_density = 0.0001
config = {
"beam_width": beam_width,
"beam_density": beam_density,
}
parser = nlp.add_pipe("beam_parser", config=config)
train_examples = []
for text, annotations in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for dep in annotations.get("deps", []):
parser.add_label(dep)
optimizer = nlp.initialize()
# run overfitting
for i in range(150):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
assert losses["beam_parser"] < 0.0001
# test the scores from the beam
test_text = "I like securities."
docs = [nlp.make_doc(test_text)]
beams = parser.predict(docs)
head_scores, label_scores = parser.scored_parses(beams)
# we only processed one document
head_scores = head_scores[0]
label_scores = label_scores[0]
# test label annotations: 0=nsubj, 2=dobj, 3=punct
assert label_scores[(0, "nsubj")] == pytest.approx(1.0, abs=eps)
assert label_scores[(0, "dobj")] == pytest.approx(0.0, abs=eps)
assert label_scores[(0, "punct")] == pytest.approx(0.0, abs=eps)
assert label_scores[(2, "nsubj")] == pytest.approx(0.0, abs=eps)
assert label_scores[(2, "dobj")] == pytest.approx(1.0, abs=eps)
assert label_scores[(2, "punct")] == pytest.approx(0.0, abs=eps)
assert label_scores[(3, "nsubj")] == pytest.approx(0.0, abs=eps)
assert label_scores[(3, "dobj")] == pytest.approx(0.0, abs=eps)
assert label_scores[(3, "punct")] == pytest.approx(1.0, abs=eps)
# test head annotations: the root is token at index 1
assert head_scores[(0, 0)] == pytest.approx(0.0, abs=eps)
assert head_scores[(0, 1)] == pytest.approx(1.0, abs=eps)
assert head_scores[(0, 2)] == pytest.approx(0.0, abs=eps)
assert head_scores[(2, 0)] == pytest.approx(0.0, abs=eps)
assert head_scores[(2, 1)] == pytest.approx(1.0, abs=eps)
assert head_scores[(2, 2)] == pytest.approx(0.0, abs=eps)
assert head_scores[(3, 0)] == pytest.approx(0.0, abs=eps)
assert head_scores[(3, 1)] == pytest.approx(1.0, abs=eps)
assert head_scores[(3, 2)] == pytest.approx(0.0, abs=eps)
# Also test the results are still the same after IO
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
nlp2 = util.load_model_from_path(tmp_dir)
docs2 = [nlp2.make_doc(test_text)]
parser2 = nlp2.get_pipe("beam_parser")
beams2 = parser2.predict(docs2)
head_scores2, label_scores2 = parser2.scored_parses(beams2)
# we only processed one document
head_scores2 = head_scores2[0]
label_scores2 = label_scores2[0]
# check the results again
assert label_scores2[(0, "nsubj")] == pytest.approx(1.0, abs=eps)
assert label_scores2[(0, "dobj")] == pytest.approx(0.0, abs=eps)
assert label_scores2[(0, "punct")] == pytest.approx(0.0, abs=eps)
assert label_scores2[(2, "nsubj")] == pytest.approx(0.0, abs=eps)
assert label_scores2[(2, "dobj")] == pytest.approx(1.0, abs=eps)
assert label_scores2[(2, "punct")] == pytest.approx(0.0, abs=eps)
assert label_scores2[(3, "nsubj")] == pytest.approx(0.0, abs=eps)
assert label_scores2[(3, "dobj")] == pytest.approx(0.0, abs=eps)
assert label_scores2[(3, "punct")] == pytest.approx(1.0, abs=eps)
assert head_scores2[(0, 0)] == pytest.approx(0.0, abs=eps)
assert head_scores2[(0, 1)] == pytest.approx(1.0, abs=eps)
assert head_scores2[(0, 2)] == pytest.approx(0.0, abs=eps)
assert head_scores2[(2, 0)] == pytest.approx(0.0, abs=eps)
assert head_scores2[(2, 1)] == pytest.approx(1.0, abs=eps)
assert head_scores2[(2, 2)] == pytest.approx(0.0, abs=eps)
assert head_scores2[(3, 0)] == pytest.approx(0.0, abs=eps)
assert head_scores2[(3, 1)] == pytest.approx(1.0, abs=eps)
assert head_scores2[(3, 2)] == pytest.approx(0.0, abs=eps)
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